A Gentle Introduction to using Support Vector Machines for Classification

Support vector machines are supervised learning models that analyse data to find patterns useful in classification and regression. They are versatile: they can identify non-linear relationships, work with discrete and continuous data, and are used for two-class classification, multi-class classification as well as regression. They are remarkable for unifying geometric theory, elegant mathematics, and theoretical guarantees with practical solid use cases. They provide several specific benefits. With the use of Kernel functions, they are highly effective in higher dimensional spaces.